public void SaveAndLoadModel()
{
///
/// Train a model
///
var xgbTrainer = new XGBoost.XGBClassifier();
int countTrainingPoints = 20;
entity.XGBArray trainClass1 = Util.GenerateRandom2dPoints(countTrainingPoints / 2, -1.0, 0.0, 0.0, 1.0, 0.0); //Top left quadrant
entity.XGBArray trainClass2 = Util.GenerateRandom2dPoints(countTrainingPoints / 2, 0.0, 1.0, -1.0, 0.0, 1.0); //Bot right quadrant
entity.XGBArray train_Class1_Class2 = Util.UnionOfXGBArray(trainClass1, trainClass2);
xgbTrainer.Fit(train_Class1_Class2.Vectors, train_Class1_Class2.Labels);
///
/// Save the model
///
string fileModel = "MyLinearModel.dat";
if (System.IO.File.Exists(fileModel))
{
System.IO.File.Delete(fileModel);
}
xgbTrainer.SaveModelToFile(fileModel);
///
/// Load the saved model
///
var xgbProduction = XGBoost.XGBClassifier.LoadClassifierFromFile(fileModel);
int countTestingPoints = 50;
entity.XGBArray testClass1 = Util.GenerateRandom2dPoints(countTestingPoints / 2, -0.8, -0.2, 0.2, 0.8, 0.0); //Top left quadrant
entity.XGBArray testClass2 = Util.GenerateRandom2dPoints(countTestingPoints / 2, 0.2, 0.8, -0.8, -0.2, 1.0); //Bot right quadrant
entity.XGBArray test_Class1_Class2 = Util.UnionOfXGBArray(testClass1, testClass2);
var results = xgbProduction.Predict(test_Class1_Class2.Vectors);
CollectionAssert.AreEqual(results, test_Class1_Class2.Labels);
}
public void TestMethod1()
{
var xgb = new XGBoost.XGBClassifier();
///
/// Generate training vectors
///
int countTrainingPoints = 50;
entity.XGBArray trainClass_0_1 = Util.GenerateRandom2dPoints(countTrainingPoints / 2,
0.0, 0.5,
0.5, 1.0, 1.0);//0,1
entity.XGBArray trainClass_1_0 = Util.GenerateRandom2dPoints(countTrainingPoints / 2,
0.5, 1.0,
0.0, 0.5, 1.0);//1,0
entity.XGBArray trainClass_0_0 = Util.GenerateRandom2dPoints(countTrainingPoints / 2,
0.0, 0.5,
0.0, 0.5, 0.0);//0,0
entity.XGBArray trainClass_1_1 = Util.GenerateRandom2dPoints(countTrainingPoints / 2,
0.5, 1.0,
0.5, 1.0, 0.0);//1,1
///
/// Train the model
///
entity.XGBArray allVectorsTraining = Util.UnionOfXGBArrays(trainClass_0_1, trainClass_1_0, trainClass_0_0, trainClass_1_1);
xgb.Fit(allVectorsTraining.Vectors, allVectorsTraining.Labels);
///
/// Test the model
///
int countTestingPoints = 10;
entity.XGBArray testClass_0_1 = Util.GenerateRandom2dPoints(countTestingPoints,
0.1, 0.4,
0.6, 0.9, 1.0);//0,1
entity.XGBArray testClass_1_0 = Util.GenerateRandom2dPoints(countTestingPoints,
0.6, 0.9,
0.1, 0.4, 1.0);//1,0
entity.XGBArray testClass_0_0 = Util.GenerateRandom2dPoints(countTestingPoints,
0.1, 0.4,
0.1, 0.4, 0.0);//0,0
entity.XGBArray testClass_1_1 = Util.GenerateRandom2dPoints(countTestingPoints,
0.6, 0.9,
0.6, 0.9, 0.0);//1,1
entity.XGBArray allVectorsTest = Util.UnionOfXGBArrays(testClass_0_1, testClass_1_0, testClass_0_0, testClass_1_1);
var resultsActual = xgb.Predict(allVectorsTest.Vectors);
CollectionAssert.AreEqual(resultsActual, allVectorsTest.Labels);
}
public void TrainAndTestIris()
{
///
/// Load training vectors
///
string filenameTrain = "Iris\\Iris.train.data";
iris.Iris[] recordsTrain = IrisUtils.LoadIris(filenameTrain);
entity.XGVector <iris.Iris>[] vectorsTrain = IrisUtils.ConvertFromIrisToFeatureVectors(recordsTrain);
///
/// Load testingvectors
///
string filenameTest = "Iris\\Iris.test.data";
iris.Iris[] recordsTest = IrisUtils.LoadIris(filenameTest);
entity.XGVector <iris.Iris>[] vectorsTest = IrisUtils.ConvertFromIrisToFeatureVectors(recordsTest);
int noOfClasses = 3;
var xgbc = new XGBoost.XGBClassifier(objective: "multi:softprob", numClass: 3);
entity.XGBArray arrTrain = Util.ConvertToXGBArray(vectorsTrain);
entity.XGBArray arrTest = Util.ConvertToXGBArray(vectorsTest);
xgbc.Fit(arrTrain.Vectors, arrTrain.Labels);
var outcomeTest = xgbc.Predict(arrTest.Vectors);
for (int index = 0; index < arrTest.Vectors.Length; index++)
{
string sExpected = IrisUtils.ConvertLabelFromNumericToString(arrTest.Labels[index]);
float[] arrResults = new float[]
{
outcomeTest[index * noOfClasses + 0],
outcomeTest[index * noOfClasses + 1],
outcomeTest[index * noOfClasses + 2]
};
float max = arrResults.Max();
int indexWithMaxValue = Util.GetIndexWithMaxValue(arrResults);
string sActualClass = IrisUtils.ConvertLabelFromNumericToString((float)indexWithMaxValue);
Trace.WriteLine($"{index} Expected={sExpected} Actual={sActualClass}");
Assert.AreEqual(sActualClass, sExpected);
}
string pathFull = System.IO.Path.Combine(Util.GetProjectDir2(), _fileModelIris);
xgbc.SaveModelToFile(pathFull);
}
public void LinearClassification2()
{
var xgb = new XGBoost.XGBClassifier();
int countTrainingPoints = 20;
entity.XGBArray trainClass1 = Util.GenerateRandom2dPoints(countTrainingPoints / 2, -1.0, 0.0, 0.0, 1.0, 0.0); //Top left quadrant
entity.XGBArray trainClass2 = Util.GenerateRandom2dPoints(countTrainingPoints / 2, 0.0, 1.0, -1.0, 0.0, 1.0); //Bot right quadrant
entity.XGBArray train_Class1_Class2 = Util.UnionOfXGBArray(trainClass1, trainClass2);
xgb.Fit(train_Class1_Class2.Vectors, train_Class1_Class2.Labels);
int countTestingPoints = 50;
entity.XGBArray testClass1 = Util.GenerateRandom2dPoints(countTestingPoints / 2, -0.8, -0.2, 0.2, 0.8, 0.0); //Top left quadrant
entity.XGBArray testClass2 = Util.GenerateRandom2dPoints(countTestingPoints / 2, 0.2, 0.8, -0.8, -0.2, 1.0); //Bot right quadrant
entity.XGBArray test_Class1_Class2 = Util.UnionOfXGBArray(testClass1, testClass2);
var results = xgb.Predict(test_Class1_Class2.Vectors);
CollectionAssert.AreEqual(results, test_Class1_Class2.Labels);
}
public void LinearClassification1()
{
var xgb = new XGBoost.XGBClassifier();
float[][] vectorsTrain = new float[][]
{
new[] { 0.5f, 0.5f },
new[] { 0.6f, 0.6f },
new[] { 0.6f, 0.4f },
new[] { 0.4f, 0.6f },
new[] { 0.4f, 0.4f },
new[] { -0.5f, -0.5f },
new[] { -0.6f, -0.6f },
new[] { -0.6f, -0.4f },
new[] { -0.4f, -0.6f },
new[] { -0.4f, -0.4f },
};
var lablesTrain = new[]
{
1.0f,
1.0f,
1.0f,
1.0f,
1.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
};
///
/// Ensure count of training labels=count of training vectors
///
Assert.AreEqual(vectorsTrain.Length, lablesTrain.Length);
///
/// Train the model
///
xgb.Fit(vectorsTrain, lablesTrain);
///
/// Test the model using test vectors
///
float[][] vectorsTest = new float[][]
{
new[] { 0.55f, 0.55f },
new[] { 0.55f, 0.45f },
new[] { 0.45f, 0.55f },
new[] { 0.45f, 0.45f },
new[] { -0.55f, -0.55f },
new[] { -0.55f, -0.45f },
new[] { -0.45f, -0.55f },
new[] { -0.45f, -0.45f },
};
var labelsTestExpected = new[]
{
1.0f,
1.0f,
1.0f,
1.0f,
0.0f,
0.0f,
0.0f,
0.0f,
};
float[] labelsTestPredicted = xgb.Predict(vectorsTest);
///
/// Verify that predicted labels match the expected labels
///
CollectionAssert.AreEqual(labelsTestPredicted, labelsTestExpected);
}
